Aerosols arguably remain the single greatest uncertainty among anthropogenic perturbations of the climate system. In particular the effects of aerosol-cloud interactions on the hydrological cycle remain highly uncertain.
Aerosol effects on precipitation have traditionally been assessed bottom-up, modelling and observing the chain of processes from aerosol emission, microphysical transformations, acting as cloud condensation and ice nuclei, via cloud microphysics and radiative effects to precipitation formation of individual clouds and cloud fields. However, this relies on a complete understanding of a very complex and uncertain process chain and has been shown to be subject to large uncertainties.
In this presentation, I will critically review some of the achievements made towards quantifying aerosol effects on precipitation - with a focus on bridging the scales.
Starting from the process scale, I will explore pathways for aerosol effects on precipitation and our ability to simulate and observationally constrain the relevant processes. I will further show that the assessment of precipitation changes in an energetic framework (because the associated change in latent heat release needs to be balanced by surface or top-of-atmosphere fluxes or compensated for by energy divergence/convergence), provides a powerful constraint on regional scale precipitation.
Dr. Philip Stier is a Professor of Atmospheric Physics in the Department of Physics at the University of Oxford, where he also heads the Atmospheric, Oceanic and Planetary Physics sub-department. His research topics cover physical aspects of the climate system, with a focus on clouds, aerosols, and radiation.
After undergraduate studies at the Ludwig-Maximilians-Universität München, Dr. Stier went to complete a Masters at the University of Reading. He obtained his PhD on global aerosol-climate modelling at the Max Planck Institute for Meteorology / University of Hamburg. After a post-doctoral stint at Caltech, Pasadena, he took on a University Lectureship in Atmospheric Physics at Oxford.
Over the years, Dr. Stier has become particularly interested in constraining their interactions with a combination of observations, process models, global models and theory - increasingly using machine learning.
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